In this session we look at how Wells Fargo implements “RAG-as-a-Service” multi-agent architecture built on Microsoft Fabric, where logs are centralized in OneLake and Databricks serves as the execution layer for orchestrating sequential agents. The architecture follows a two-stage pattern inspired by real-world incident triage: a Log Retrieval Agent that queries and assembles relevant context from Lakehouse tables using hybrid retrieval, followed by a Root Cause Processing Agent that consumes this context to generate structured summaries and recommended next steps, with all intermediate outputs persisted back into Fabric for governance, lineage, and observability.

Key Highlights:

Fabric OneLake as the governed context backbone for enterprise logs and metadata

Reusable RAG-as-a-Service layer exposing context retrieval and management APIs

Multi-agent orchestration: Log Retrieval Agent followed by Root Cause Processing Agent

Hybrid retrieval combining Lakehouse SQL filtering with semantic similarity search

Full lineage and auditability by persisting agent inputs and outputs back into Fabric tables

This session emphasizes a clear, enterprise-ready architecture rather than isolated AI demos.
Attendees will gain a concrete blueprint for implementing governed, multi-agent RAG workflows directly on Microsoft Fabric.